sep_smooths_year2
Load all required libraries.
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.3 v dplyr 1.0.7
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 2.0.0 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutlibrary(broom)Read in raw data from RDS.
raw_data <- readRDS("./year2.RDS")Make a few small modifications to names and data for visualizations.
final_data <- raw_data %>% mutate(log_copy_per_L = log10(mean_copy_num_L)) %>%
rename(Facility = wrf) %>%
mutate(Facility = recode(Facility,
"NO" = "WRF A",
"MI" = "WRF B",
"CC" = "WRF C"))Seperate the data by gene target to ease layering in the final plot
#make three data layers
only_positives <<- subset(final_data, (!is.na(final_data$Facility)))
only_n1 <- subset(only_positives, target == "N1")
only_n2 <- subset(only_positives, target == "N2")
only_background <<-final_data %>%
select(c(date, cases_cum_clarke, new_cases_clarke, X7_day_ave_clarke)) %>%
group_by(date) %>% summarise_if(is.numeric, mean)
#specify fun colors
background_color <- "#7570B3"
seven_day_ave_color <- "#E6AB02"
marker_colors <- c("N1" = '#1B9E77',"N2" ='#D95F02')#remove facilty C for now
#only_n1 <- only_n1[!(only_n1$Facility == "WRF C"),]
#only_n2 <- only_n2[!(only_n2$Facility == "WRF C"),]
only_n1 <- only_n1[!(only_n1$Facility == "WRF A" & only_n1$date == "2020-11-02"), ]
only_n2 <- only_n2[!(only_n2$Facility == "WRF A" & only_n2$date == "2020-11-02"), ]Build the main plot
#first layer is the background epidemic curve
p1 <- only_background %>%
plotly::plot_ly() %>%
plotly::add_trace(x = ~date, y = ~new_cases_clarke,
type = "bar",
hoverinfo = "text",
text = ~paste('</br> Date: ', date,
'</br> Daily Cases: ', new_cases_clarke),
alpha = 0.5,
name = "Daily Reported Cases",
color = background_color,
colors = background_color,
showlegend = FALSE) %>%
layout(yaxis = list(title = "Clarke County Daily Cases", showline=TRUE)) %>%
layout(legend = list(orientation = "h", x = 0.2, y = -0.3))
#renders the main plot layer two as seven day moving average
p1 <- p1 %>% plotly::add_trace(x = ~date, y = ~X7_day_ave_clarke,
type = "scatter",
mode = "lines",
hoverinfo = "text",
text = ~paste('</br> Date: ', date,
'</br> Seven-Day Moving Average: ', X7_day_ave_clarke),
name = "Seven Day Moving Average Athens",
line = list(color = seven_day_ave_color),
showlegend = FALSE)
#renders the main plot layer three as positive target hits
p2 <- plotly::plot_ly() %>%
plotly::add_trace(x = ~date, y = ~mean_copy_num_L,
type = "scatter",
mode = "markers",
hoverinfo = "text",
text = ~paste('</br> Date: ', date,
'</br> Facility: ', Facility,
'</br> Target: ', target,
'</br> Copies/L: ', round(mean_copy_num_L, digits = 2)),
data = only_n1,
symbol = ~Facility,
marker = list(color = '#1B9E77', size = 8, opacity = 0.65),
showlegend = FALSE) %>%
plotly::add_trace(x = ~date, y = ~mean_copy_num_L,
type = "scatter",
mode = "markers",
hoverinfo = "text",
text = ~paste('</br> Date: ', date,
'</br> Facility: ', Facility,
'</br> Target: ', target,
'</br> Copies/L: ', round(mean_copy_num_L, digits = 2)),
data = only_n2,
symbol = ~Facility,
marker = list(color = '#D95F02', size = 8, opacity = 0.65),
showlegend = FALSE) %>%
layout(yaxis = list(title = "SARS CoV-2 Copies/L",
showline = TRUE,
type = "log",
dtick = 1,
automargin = TRUE)) %>%
layout(legend = list(orientation = "h", x = 0.2, y = -0.3))
#adds the limit of detection dashed line
p2 <- p2 %>% plotly::add_segments(x = as.Date("2021-06-30"),
xend = ~max(date + 10),
y = 3571.429, yend = 3571.429,
opacity = 0.35,
line = list(color = "black", dash = "dash")) %>%
layout(annotations = list(x = as.Date("2021-06-30"), y = 3.8, xref = "x", yref = "y",
text = "Limit of Detection", showarrow = FALSE))
p1 p2Combine the two main plot pieces as a subplot
#seperate n1 and n2 frames by site
#n1
wrf_a_only_n1 <- subset(only_n1, Facility == "WRF A")
wrf_b_only_n1 <- subset(only_n1, Facility == "WRF B")
wrf_c_only_n1 <- subset(only_n1, Facility == "WRF C")
#n2
wrf_a_only_n2 <- subset(only_n2, Facility == "WRF A")
wrf_b_only_n2 <- subset(only_n2, Facility == "WRF B")
wrf_c_only_n2 <- subset(only_n2, Facility == "WRF C")
#rejoin the old data frames then seperate in to averages for each plant.
wrfa_both <- full_join(wrf_a_only_n1, wrf_a_only_n2)%>%
select(c(date, mean_total_copies)) %>%
group_by(date) %>%
summarize_if(is.numeric, mean) %>%
ungroup() %>%
mutate(log_total_copies_both = log10(mean_total_copies))## Joining, by = c("date", "new_cases_clarke", "cases_cum_clarke", "X7_day_ave_clarke", "Facility", "collection_num", "target", "mean_copy_num_uL_rxn", "mean_copy_num_L", "sd_L", "mean_total_copies", "sd_total_copies", "log_copy_per_L")wrfb_both <- full_join(wrf_b_only_n1, wrf_b_only_n2)%>%
select(c(date, mean_total_copies)) %>%
group_by(date) %>%
summarize_if(is.numeric, mean) %>%
ungroup() %>%
mutate(log_total_copies_both = log10(mean_total_copies))## Joining, by = c("date", "new_cases_clarke", "cases_cum_clarke", "X7_day_ave_clarke", "Facility", "collection_num", "target", "mean_copy_num_uL_rxn", "mean_copy_num_L", "sd_L", "mean_total_copies", "sd_total_copies", "log_copy_per_L")wrfc_both <- full_join(wrf_c_only_n1, wrf_c_only_n2)%>%
select(c(date, mean_total_copies)) %>%
group_by(date) %>%
summarize_if(is.numeric, mean) %>%
ungroup() %>%
mutate(log_total_copies_both = log10(mean_total_copies))## Joining, by = c("date", "new_cases_clarke", "cases_cum_clarke", "X7_day_ave_clarke", "Facility", "collection_num", "target", "mean_copy_num_uL_rxn", "mean_copy_num_L", "sd_L", "mean_total_copies", "sd_total_copies", "log_copy_per_L")#get max date
maxdate <- max(wrfa_both$date)
mindate <- min(wrfa_both$date)Build loess smoothing figures figures
This makes the individual plots
#**************************************WRF A PLOT**********************************************
#add trendlines
#extract data from geom_smooth
#both extract
# *********************************span 0.6***********************************
#*****************Must always update the n = TOTAL NUMBER OF DAYS*************************
extract_botha <- ggplot(wrfa_both, aes(x = date, y = log_total_copies_both)) +
stat_smooth(aes(outfit=fit_botha<<-..y..), method = "loess", color = '#1B9E77',
span = 0.3, n = 197)## Warning: Ignoring unknown aesthetics: outfit#look at the fits to align dates and total observations
#both
extract_botha## `geom_smooth()` using formula 'y ~ x'
fit_botha## [1] 11.53411 11.57188 11.60934 11.64656 11.68357 11.72044 11.75721 11.79383
## [9] 11.83021 11.86635 11.90223 11.93787 11.97324 12.00836 12.04321 12.07821
## [17] 12.11368 12.14943 12.18530 12.22109 12.25665 12.29178 12.32633 12.36011
## [25] 12.39294 12.42465 12.45506 12.48265 12.50678 12.52868 12.54955 12.57061
## [33] 12.59305 12.61810 12.64438 12.66991 12.69498 12.71988 12.74492 12.77038
## [41] 12.79656 12.82481 12.85565 12.88810 12.92120 12.95400 12.98552 13.01481
## [49] 13.04090 13.06282 13.08425 13.10832 13.13327 13.15735 13.17878 13.19582
## [57] 13.20670 13.21261 13.21595 13.21677 13.21512 13.21106 13.20465 13.19593
## [65] 13.18357 13.16660 13.14558 13.12109 13.09371 13.06399 13.03253 12.99988
## [73] 12.96663 12.93334 12.90059 12.86895 12.83595 12.79936 12.76020 12.71945
## [81] 12.67813 12.63723 12.59776 12.56069 12.52705 12.49451 12.46049 12.42563
## [89] 12.39053 12.35584 12.32216 12.29012 12.26034 12.23345 12.21008 12.19083
## [97] 12.17633 12.16937 12.17045 12.17679 12.18562 12.19417 12.19965 12.19930
## [105] 12.19615 12.19471 12.19438 12.19457 12.19467 12.19408 12.19222 12.18908
## [113] 12.18523 12.18095 12.17651 12.17219 12.16826 12.16500 12.16267 12.16155
## [121] 12.16102 12.16037 12.15978 12.15944 12.15953 12.16023 12.16172 12.16418
## [129] 12.16779 12.17273 12.17919 12.18733 12.19950 12.21687 12.23791 12.26109
## [137] 12.28485 12.30768 12.32803 12.34436 12.35515 12.36175 12.36666 12.36998
## [145] 12.37186 12.37242 12.37179 12.37009 12.36745 12.36400 12.35987 12.35518
## [153] 12.35006 12.33829 12.31659 12.28957 12.26178 12.23784 12.22231 12.21977
## [161] 12.22499 12.23011 12.23584 12.24287 12.25190 12.26362 12.27875 12.29797
## [169] 12.32199 12.35045 12.38233 12.41726 12.45489 12.49487 12.53684 12.58046
## [177] 12.62536 12.67121 12.71763 12.76428 12.81081 12.85687 12.90209 12.94613
## [185] 12.98864 13.02925 13.06762 13.10340 13.13748 13.17099 13.20393 13.23629
## [193] 13.26808 13.29929 13.32992 13.35996 13.38942#assign fits to a vector
both_trenda <- fit_botha
#extract y min and max for each
limits_botha <- ggplot_build(extract_botha)$data## `geom_smooth()` using formula 'y ~ x'limits_botha <- as.data.frame(limits_botha)
both_ymina <- limits_botha$ymin
both_ymaxa <- limits_botha$ymax
#reassign dataframes (just to be safe)
work_botha <- wrfa_both
#fill in missing dates to smooth fits
work_botha <- work_botha %>% complete(date = seq(min(date), max(date), by = "1 day"))
date_vec_botha <- work_botha$date
#create a new smooth dataframe to layer
smooth_frame_botha <- data.frame(date_vec_botha, both_trenda, both_ymina, both_ymaxa)#WRF A
#plot smooth frames
p_wrf_a <- plotly::plot_ly() %>%
plotly::add_lines(x = ~date_vec_botha, y = ~both_trenda,
data = smooth_frame_botha,
hoverinfo = "text",
text = ~paste('</br> Date: ', date_vec_botha,
'</br> Median Log Copies: ', round(both_trenda, digits = 2)),
line = list(color = '#1B9E77', size = 8, opacity = 0.65),
showlegend = FALSE) %>%
layout(xaxis = list(range = c(mindate - 7, maxdate + 7))) %>% #buffer here
plotly::add_ribbons(x ~date_vec_botha, ymin = ~both_ymina, ymax = ~both_ymaxa,
showlegend = FALSE,
opacity = 0.25,
hoverinfo = "text",
text = ~paste('</br> Date: ', date_vec_botha, #leaving in case we want to change
'</br> Max Log Copies: ', round(both_ymaxa, digits = 2),
'</br> Min Log Copies: ', round(both_ymina, digits = 2)),
name = "",
fillcolor = '#1B9E77',
line = list(color = '#1B9E77')) %>%
layout(yaxis = list(title = "Total Log10 SARS CoV-2 Copies",
showline = TRUE,
automargin = TRUE)) %>%
layout(xaxis = list(title = "Date")) %>%
layout(title = "WRF A") %>%
plotly::add_markers(x = ~date, y = ~log_total_copies_both,
data = wrfa_both,
hoverinfo = "text",
showlegend = FALSE,
text = ~paste('</br> Date: ', date,
'</br> Actual Log Copies: ', round(log_total_copies_both, digits = 2)),
marker = list(color = '#1B9E77', size = 6, opacity = 0.65))
p_wrf_asave(p_wrf_a, file = "./site_objects/wrf_a_year2.rda")#**************************************WRF B PLOT**********************************************
#add trendlines
#extract data from geom_smooth
#both extract
# *********************************span 0.6***********************************
#*****************Must always update the n = TOTAL NUMBER OF DAYS*************************
extract_bothb <- ggplot(wrfb_both, aes(x = date, y = log_total_copies_both)) +
stat_smooth(aes(outfit=fit_bothb<<-..y..), method = "loess", color = '#D95F02',
span = 0.3, n = 197)## Warning: Ignoring unknown aesthetics: outfit#look at the fits to align dates and total observations
#both
extract_bothb## `geom_smooth()` using formula 'y ~ x'
fit_bothb## [1] 10.69304 10.78810 10.88108 10.97207 11.06118 11.14853 11.23423 11.31831
## [9] 11.40066 11.48119 11.55980 11.63639 11.71086 11.78310 11.85303 11.92090
## [17] 11.98700 12.05127 12.11365 12.17408 12.23249 12.28883 12.34303 12.39503
## [25] 12.44477 12.49219 12.53722 12.58028 12.62169 12.66123 12.69869 12.73387
## [33] 12.76654 12.79651 12.82084 12.83810 12.85032 12.85952 12.86772 12.87694
## [41] 12.88921 12.90215 12.91240 12.92047 12.92688 12.93215 12.93680 12.94135
## [49] 12.94632 12.95224 12.96078 12.97228 12.98526 12.99819 13.00958 13.01794
## [57] 13.02174 13.02011 13.01403 13.00487 12.99397 12.98268 12.97235 12.96432
## [65] 12.95730 12.94925 12.94039 12.93096 12.92119 12.91132 12.90158 12.89221
## [73] 12.88343 12.87549 12.86862 12.86304 12.86056 12.86191 12.86573 12.87064
## [81] 12.87528 12.87828 12.87828 12.87391 12.86380 12.84788 12.82744 12.80319
## [89] 12.77580 12.74598 12.71443 12.68183 12.64889 12.61629 12.58473 12.55491
## [97] 12.52752 12.50080 12.47295 12.44461 12.41644 12.38908 12.36317 12.33938
## [105] 12.32259 12.31463 12.31155 12.30939 12.30420 12.29201 12.26888 12.23424
## [113] 12.19166 12.14339 12.09169 12.03881 11.98700 11.93852 11.89563 11.86056
## [121] 11.82838 11.79358 11.75758 11.72183 11.68774 11.65674 11.63028 11.60978
## [129] 11.59666 11.59237 11.59832 11.61595 11.65171 11.70758 11.77834 11.85874
## [137] 11.94356 12.02757 12.10552 12.17219 12.22234 12.26258 12.30273 12.34237
## [145] 12.38108 12.41843 12.45400 12.48737 12.51812 12.54583 12.57007 12.59042
## [153] 12.60646 12.61155 12.60290 12.58592 12.56603 12.54863 12.53912 12.54291
## [161] 12.55418 12.56406 12.57337 12.58292 12.59353 12.60600 12.62115 12.63981
## [169] 12.66277 12.68942 12.71838 12.74929 12.78183 12.81566 12.85044 12.88584
## [177] 12.92151 12.95712 12.99234 13.02683 13.06024 13.09225 13.12252 13.15071
## [185] 13.17648 13.19949 13.21942 13.23592 13.24989 13.26247 13.27366 13.28345
## [193] 13.29185 13.29887 13.30450 13.30875 13.31162#assign fits to a vector
both_trendb <- fit_bothb
#extract y min and max for each
limits_bothb <- ggplot_build(extract_bothb)$data## `geom_smooth()` using formula 'y ~ x'limits_bothb <- as.data.frame(limits_bothb)
both_yminb <- limits_bothb$ymin
both_ymaxb <- limits_bothb$ymax
#reassign dataframes (just to be safe)
work_bothb <- wrfb_both
#fill in missing dates to smooth fits
work_bothb <- work_bothb %>% complete(date = seq(min(date), max(date), by = "1 day"))
date_vec_bothb <- work_bothb$date
#create a new smooth dataframe to layer
smooth_frame_bothb <- data.frame(date_vec_bothb, both_trendb, both_yminb, both_ymaxb)#WRF B
#plot smooth frames
p_wrf_b <- plotly::plot_ly() %>%
plotly::add_lines(x = ~date_vec_bothb, y = ~both_trendb,
data = smooth_frame_bothb,
hoverinfo = "text",
text = ~paste('</br> Date: ', date_vec_bothb,
'</br> Median Log Copies: ', round(both_trendb, digits = 2)),
line = list(color = '#D95F02', size = 8, opacity = 0.65),
showlegend = FALSE) %>%
layout(xaxis = list(range = c(mindate - 7, maxdate + 7))) %>% #buffer here
plotly::add_ribbons(x ~date_vec_bothb, ymin = ~both_yminb, ymax = ~both_ymaxb,
showlegend = FALSE,
opacity = 0.25,
hoverinfo = "text",
text = ~paste('</br> Date: ', date_vec_bothb, #leaving in case we want to change
'</br> Max Log Copies: ', round(both_ymaxb, digits = 2),
'</br> Min Log Copies: ', round(both_yminb, digits = 2)),
name = "",
fillcolor = '#D95F02',
line = list(color = '#D95F02')) %>%
layout(yaxis = list(title = "Total Log10 SARS CoV-2 Copies",
showline = TRUE,
automargin = TRUE)) %>%
layout(xaxis = list(title = "Date")) %>%
layout(title = "WRF B") %>%
plotly::add_markers(x = ~date, y = ~log_total_copies_both,
data = wrfb_both,
hoverinfo = "text",
showlegend = FALSE,
text = ~paste('</br> Date: ', date,
'</br> Actual Log Copies: ', round(log_total_copies_both, digits = 2)),
marker = list(color = '#D95F02', size = 6, opacity = 0.65))
p_wrf_bsave(p_wrf_b, file = "./site_objects/wrf_b_year2.rda")#**************************************WRF C PLOT********************************************** #add trendlines #extract data from geom_smooth # *********************************span 0.6*********************************** #*****************Must always update the n = TOTAL NUMBER OF DAYS*************************
extract_bothc <- ggplot(wrfc_both, aes(x = date, y = log_total_copies_both)) +
stat_smooth(aes(outfit=fit_bothc<<-..y..), method = "loess", color = '#E7298A',
span = 0.3, n = 197)## Warning: Ignoring unknown aesthetics: outfit#look at the fits to align dates and total observations
#both
extract_bothc## `geom_smooth()` using formula 'y ~ x'
fit_bothc## [1] 10.72504 10.80872 10.89021 10.96928 11.04570 11.11923 11.18965 11.25713
## [9] 11.32206 11.38450 11.44453 11.50221 11.55761 11.61081 11.66186 11.70943
## [17] 11.75248 11.79153 11.82713 11.85981 11.89009 11.91852 11.94563 11.97195
## [25] 11.99801 12.02436 12.05152 12.08010 12.10984 12.13995 12.16964 12.19813
## [33] 12.22463 12.24836 12.26879 12.28656 12.30270 12.31822 12.33414 12.35148
## [41] 12.37124 12.39329 12.41644 12.44016 12.46392 12.48719 12.50943 12.53011
## [49] 12.54871 12.56468 12.57848 12.59101 12.60240 12.61279 12.62234 12.63118
## [57] 12.63945 12.65096 12.66769 12.68703 12.70641 12.72323 12.73489 12.73879
## [65] 12.73500 12.72580 12.71200 12.69440 12.67380 12.65100 12.62680 12.60201
## [73] 12.57742 12.55383 12.53205 12.51286 12.49509 12.47703 12.45873 12.44028
## [81] 12.42171 12.40310 12.38450 12.36597 12.34758 12.32851 12.30808 12.28656
## [89] 12.26421 12.24128 12.21804 12.19475 12.17166 12.14904 12.12714 12.10624
## [97] 12.08658 12.06483 12.03895 12.01113 11.98359 11.95852 11.93812 11.92460
## [105] 11.91740 11.91387 11.91307 11.91407 11.91594 11.91775 11.91856 11.92348
## [113] 11.93669 11.95584 11.97860 12.00264 12.02562 12.04520 12.05905 12.06482
## [121] 12.06214 12.05293 12.03835 12.01955 11.99769 11.97392 11.94940 11.92527
## [129] 11.90270 11.88283 11.86682 11.85583 11.85018 11.84878 11.85062 11.85469
## [137] 11.85999 11.86550 11.87024 11.87318 11.87332 11.87110 11.86778 11.86360
## [145] 11.85877 11.85353 11.84811 11.84274 11.83765 11.83307 11.82923 11.82636
## [153] 11.82470 11.82012 11.81029 11.79811 11.78647 11.77826 11.77638 11.78371
## [161] 11.79566 11.80630 11.81656 11.82735 11.83960 11.85423 11.87215 11.89429
## [169] 11.92156 11.95371 11.98961 12.02887 12.07114 12.11603 12.16318 12.21221
## [177] 12.26275 12.31442 12.36686 12.41969 12.47255 12.52504 12.57682 12.62749
## [185] 12.67669 12.72405 12.76920 12.81176 12.85264 12.89304 12.93296 12.97240
## [193] 13.01139 13.04994 13.08805 13.12573 13.16301#assign fits to a vector
both_trendc <- fit_bothc
#extract y min and max for each
limits_bothc <- ggplot_build(extract_bothc)$data## `geom_smooth()` using formula 'y ~ x'limits_bothc <- as.data.frame(limits_bothc)
both_yminc <- limits_bothc$ymin
both_ymaxc <- limits_bothc$ymax
#reassign dataframes (just to be safe)
work_bothc <- wrfc_both
#fill in missing dates to smooth fits
work_bothc <- work_bothc %>% complete(date = seq(min(date), max(date), by = "1 day"))
date_vec_bothc <- work_bothc$date
#create a new smooth dataframe to layer
smooth_frame_bothc <- data.frame(date_vec_bothc, both_trendc, both_yminc, both_ymaxc)#WRF C
#plot smooth frames
p_wrf_c <- plotly::plot_ly() %>%
plotly::add_lines(x = ~date_vec_bothc, y = ~both_trendc,
data = smooth_frame_bothc,
hoverinfo = "text",
text = ~paste('</br> Date: ', date_vec_bothc,
'</br> Median Log Copies: ', round(both_trendc, digits = 2)),
line = list(color = '#E7298A', size = 8, opacity = 0.65),
showlegend = FALSE) %>%
layout(xaxis = list(range = c(mindate - 7, maxdate + 7))) %>% #buffer here
plotly::add_ribbons(x ~date_vec_bothc, ymin = ~both_yminc, ymax = ~both_ymaxc,
showlegend = FALSE,
opacity = 0.25,
hoverinfo = "text",
text = ~paste('</br> Date: ', date_vec_bothc, #leaving in case we want to change
'</br> Max Log Copies: ', round(both_ymaxc, digits = 2),
'</br> Min Log Copies: ', round(both_yminc, digits = 2)),
name = "",
fillcolor = '#E7298A',
line = list(color = '#E7298A')) %>%
layout(yaxis = list(title = "Total Log10 SARS CoV-2 Copies",
showline = TRUE,
automargin = TRUE)) %>%
layout(xaxis = list(title = "Date")) %>%
layout(title = "WRF C") %>%
plotly::add_markers(x = ~date, y = ~log_total_copies_both,
data = wrfc_both,
hoverinfo = "text",
showlegend = FALSE,
text = ~paste('</br> Date: ', date,
'</br> Actual Log Copies: ', round(log_total_copies_both, digits = 2)),
marker = list(color = '#E7298A', size = 6, opacity = 0.65))
p_wrf_csave(p_wrf_c, file = "./site_objects/wrf_c_year2.rda")keeping in case
#save(wrfa_both, file = "./plotly_objs/wrfa_both.rda")
#save(wrfb_both, file = "./plotly_objs/wrfb_both.rda")
#save(wrfc_both, file = "./plotly_objs/wrfc_both.rda")#save(date_vec_botha, file = "./plotly_objs/date_vec_botha.rda")
#save(date_vec_bothb, file = "./plotly_objs/date_vec_bothb.rda")
#save(date_vec_bothc, file = "./plotly_objs/date_vec_bothc.rda")#save(both_ymina, file = "./plotly_objs/both_ymina.rda")
#save(both_ymaxa, file = "./plotly_objs/both_ymaxa.rda")
#save(both_yminb, file = "./plotly_objs/both_yminb.rda")
#save(both_ymaxb, file = "./plotly_objs/both_ymaxb.rda")
#save(both_yminc, file = "./plotly_objs/both_yminc.rda")
#save(both_ymaxc, file = "./plotly_objs/both_ymaxc.rda")